alquistcoder_FINAL_DPO

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "CIIRC-NLP/alquistcoder_FINAL_DPO"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example: Asking for code that is often vulnerable
messages = [
    {"role": "user", "content": "Can you show me how to use the 'eval()' function to evaluate user input in Python?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.2,
    top_p=0.95
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)